.csv feeds

.csv files can be comma separated, tab/bar separated and must available to retrieve via HTTP(S). Within the product feed, 1 item should equal 1 row (e.g. all data and attributes for one product should be within one row). All data field names should be unique (i.e. there should not be two columns named 'price'.

All product feeds are unique but they should all share similar properties and structure. A standard Google Shopping Feed contains these data fields:

  • ID

  • mpn / gtin

  • title

  • description

  • link

  • image link (+ any additional product images)

  • availability

  • stock level

  • days in feed / date added to feed (required for new in)

  • price

  • sale price

  • colour

  • gender

  • size

  • mpn / gtin

  • master product category (main category e.g. Clothing, Accessories, Shoes, Beauty, Homeware)

  • product type category (e.g. dress, skirt, coat, shirt)

  • custom labels (can be used to flag products for promotions / events etc.)

New In

To automate new in products, we need to be able to identify that a product is 'new', or that it has been recently added to the feed. The best way to manage new in is by a 'date added' field in the feed. Simply, this is the date that the product was inserted into the product catalogue for the first time. Alternatively, if a date is not possible, some brands use a 'newness' value which indicates how many days a product has been in the feed where 0 = new.

Where there is a date column in the feed, Kickdynamic can order the product feed by date so that products pull through in the order from newest to oldest.

Here's an example of a date added field in a product feed:

If a date / days in feed column is not an option, it is advised to use a custom label and a 'new' flag. This flag can then be used to filter and find products flagged as new. This requires more management and will need regular updates.

Important note: If out of stock products are removed from the feed, when they come back into stock they should not be time stamped as back in stock date, but as the original date added.

Product Categories / classifications

In order to automate products accurately, they must be accurately, consistently and clearly categorised. Here is an example:

Each product has a:

  • Gender

  • master product category (main category e.g. Clothing, Accessories, Shoes, Beauty, Homeware)

  • product type category (e.g. dress, skirt, coat, shirt)

  • fit (where applicable)

  • size

  • custom label